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About miHub®
Maximising Researchers' Exploration and Achievements through Materials Informatics
Vision
Turning Data and Knowledge
into Digital Assets
With intuitive data analysis functions, miHub® seamlessly supports everything from organising experimental data to accumulating new knowledge. While leveraging domain expertise, it drives data-driven hypothesis testing using statistical and machine learning techniques. This deepens the understanding of research themes and guides researchers towards their next breakthrough steps.
Furthermore, miHub® enables the accumulation of development-related data and know-how in reusable forms, fostering digital utilisation across the entire organisation. Combined with comprehensive support for developers, it dramatically accelerates R&D powered by Materials Informatics.
Conventional Approach
Before
Data-Driven Approach
After
miHub® transforms accumulated data and knowledge into digital assets, empowering researchers to deepen and broaden their exploration.
Solutions
01Integrated Data Workflow
Integrated Data Workflow
Streamline data organization across development teams and seamlessly convert it into MI-ready datasets. Manage every step of experimental planning—data registration, analysis setup, point selection, and result interpretation—through an intuitive workflow.
By preventing data handling from becoming the goal, miHub® ensures researchers and teams can focus on what truly matters: analysis, interpretation, and the creative side of development.
02Design of Experiment (DoE)
Design of Experiment (DoE)
Bayesian optimisation and machine learning accelerate the exploration of experimental points, while deepening understanding of development themes. By strategically acquiring high-value data, researchers gain richer insights and maximise the impact of their data.
For newcomers, miHub guides exploration strategies and reflection, fostering habits of scientific reasoning. For experts, it offers the freedom to design analyses that integrate their domain knowledge, enabling higher-impact results.
03Data Intelligence (DI)
Data Intelligence (DI)
Integrating development themes with analysis projects and linking them to verification items provides a holistic view of experimental data and related information. Visualisation of factor relationships helps researchers intuitively identify what to analyse and validate.
By uncovering hidden structures and causal links in complex data, it directs focus quickly to the most critical and influential factors.
04Collaboration
Collaboration
Connect analysis, experimentation, and reflection—accumulate data and share activities directly within the tool. This streamlines collaboration among researchers and supervisors, while intuitive views of project context and progress raise the quality of communication.
By making analysis processes traceable, know-how spreads across teams, fostering wider adoption of MI. Data value is no longer siloed with individuals but harnessed as a shared organisational asset.
05Customer Success
Customer Success
Our dedicated data scientists support each R&D theme, ensuring researchers and analysts gain confidence in mastering miHub®. Through tailored training and seminars, we foster reliable skill development.
With proven experience supporting over 100 companies, we provide best practices for MI adoption and integration, customised to each organisation’s structure and stage. Our optimised support framework empowers sustained growth and tangible outcomes.
Integrated Data Workflow
Streamline data organization across development teams and seamlessly convert it into MI-ready datasets. Manage every step of experimental planning—data registration, analysis setup, point selection, and result interpretation—through an intuitive workflow.
By preventing data handling from becoming the goal, miHub® ensures researchers and teams can focus on what truly matters: analysis, interpretation, and the creative side of development.
Design of Experiment (DoE)
Bayesian optimisation and machine learning accelerate the exploration of experimental points, while deepening understanding of development themes. By strategically acquiring high-value data, researchers gain richer insights and maximise the impact of their data.
For newcomers, miHub guides exploration strategies and reflection, fostering habits of scientific reasoning. For experts, it offers the freedom to design analyses that integrate their domain knowledge, enabling higher-impact results.
Data Intelligence (DI)
Integrating development themes with analysis projects and linking them to verification items provides a holistic view of experimental data and related information. Visualisation of factor relationships helps researchers intuitively identify what to analyse and validate.
By uncovering hidden structures and causal links in complex data, it directs focus quickly to the most critical and influential factors.
Collaboration
Connect analysis, experimentation, and reflection—accumulate data and share activities directly within the tool. This streamlines collaboration among researchers and supervisors, while intuitive views of project context and progress raise the quality of communication.
By making analysis processes traceable, know-how spreads across teams, fostering wider adoption of MI. Data value is no longer siloed with individuals but harnessed as a shared organisational asset.
Customer Success
Our dedicated data scientists support each R&D theme, ensuring researchers and analysts gain confidence in mastering miHub®. Through tailored training and seminars, we foster reliable skill development.
With proven experience supporting over 100 companies, we provide best practices for MI adoption and integration, customised to each organisation’s structure and stage. Our optimised support framework empowers sustained growth and tangible outcomes.
Features
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Bayesian Optimisation
It enables the discovery of optimal conditions with fewer experiments. By efficiently exploring the search space, researchers can strategically acquire high-value data and gain useful insights that lead to improved material properties and process optimisation.
Backed by a development team whose research has been presented at top international conferences in artificial intelligence, we deliver practical and highly reliable technology.
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Predictive Modelling for Candidate Exploration
We build predictive models from experimental data to evaluate candidate points in a comprehensive manner. Techniques for visualising high-dimensional spaces reveal hidden structures and patterns, enhancing the understanding of results. Model interpretation further provides insights into factor relationships and trade-offs.
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Data Management
Easily convert experimental tables into datasets optimised for MI analysis. Reduce the burden of complex data preparation, speed up data utilisation, and seamlessly link with related databases for unified management and efficient use of experimental data.
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Development Management
Connect verification items with analysis projects to manage overall progress in an integrated way. Gain a holistic view of data and results, share insights, and visualise factor relationships for faster, more strategic decisions. Shared progress across the team builds the foundation for seamless collaboration.
and more...
User Voices
